title: Optimizing low-frequency common spatial pattern features for multi-class classification of hand movement directions.
abstract: Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank common spatial pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fisher's linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.
authors.forename: Andrew Keong
authors.initials: AK
authors.lastname: Ng
authors.forename: Kai Keng
authors.initials: KK
authors.lastname: Ang
authors.forename: Keng Peng
authors.initials: KP
authors.lastname: Tee
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D004569:Electroencephalography; D017143:Fuzzy Logic; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement; D012815:Signal Processing, Computer-Assisted